With the increased use of genetic testing, whether through broadening of testing criteria or the well documented ordering of tests by physicians because of patient demand, and furthermore the commoditization of measurements of thousands of variants on a single individual, the number of healthy individuals undergoing genetic testing is accelerating. The growth in the number of false positives is projected to rise dramatically because many of the current annotations for known mutations have not been developed for the asymptomatic, well population. With the false positive results, patients will be alarmed unnecessarily, and also clinicians will order unnecessary and occasionally risky tests. Furthermore, insurance companies will find the growth in unnecessary secondary testing triggered by this tsunami of false positive tests ("the incidentalome") as an unanticipated financial risk and thereby endanger the very real benefits that the sound use of genetic testing and long-anticipated genomically-enable "personalized medicine" can bring. We propose to demonstrate that this risk of the incidentalome is substantial by automatically mining the biomedical literature, by scanning public genomic data sets, and computationally predicting the effects of mutations to identify a set of candidate mutations that are predicted to not contribute to disease in the general population. This despite their annotation in authoritative genetic databases and texts as "highly penetrant" in causing disease congenitally or in childhood. Two thousand adult patients known not to have these diseases will be then tested for each of these candidate mutations with the hypothesis that we will demonstrate the presence of some of these mutations in these health individuals. Evidence supporting this hypothesis will both serve as an important caution in the use of genetic testing and will also demonstrate the value of population studies to find the broader clinical significance of genetic mutations.